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Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot Food Detection

文献类型：期刊论文

作者Zhou, Pengfei2,3; Min, Weiqing1,2,3; Song, Jiajun2,3; Zhang, Yang2,3; Jiang, Shuqiang1,2,3
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2024
卷号33页码:1285-1298
关键词Semantics Feature extraction Visualization Annotations Correlation Training Task analysis Food detection zero-shot detection food computing object detection zero-shot learning
ISSN号1057-7149
DOI10.1109/TIP.2024.3360899
英文摘要Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task of Zero-Shot Food Detection (ZSFD) by introducing FOWA dataset with rich attribute annotations. Unlike ZSD, fine-grained problems in ZSFD like inter-class similarity make synthesized features inseparable. The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories. To address these problems, we propose a novel framework ZSFDet to tackle fine-grained problems by exploiting the interaction between complex attributes. Specifically, we model the correlation between food categories and attributes in ZSFDet by multi-source graphs to provide prior knowledge for distinguishing fine-grained features. Within ZSFDet, Knowledge-Enhanced Feature Synthesizer (KEFS) learns knowledge representation from multiple sources (e.g., ingredients correlation from knowledge graph) via the multi-source graph fusion. Conditioned on the fusion of semantic knowledge representation, the region feature diffusion model in KEFS can generate fine-grained features for training the effective zero-shot detector. Extensive evaluations demonstrate the superior performance of our method ZSFDet on FOWA and the widely-used food dataset UECFOOD-256, with significant improvements by 1.8% and 3.7% ZSD mAP compared with the strong baseline RRFS. Further experiments on PASCAL VOC and MS COCO prove that enhancement of the semantic knowledge can also improve the performance on general ZSD. Code and dataset are available at https://github.com/LanceZPF/KEFS.
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001173850100003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/38710]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Min, Weiqing
作者单位1.Chinese Acad Sci, Inst Intelligent Comp Technol, Suzhou 215124, Peoples R China
2.Univ Chinese Acad Sci, Coll Comp Sci & Technol, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Pengfei,Min, Weiqing,Song, Jiajun,et al. Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot Food Detection[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2024,33:1285-1298.
APA Zhou, Pengfei,Min, Weiqing,Song, Jiajun,Zhang, Yang,&Jiang, Shuqiang.(2024).Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot Food Detection.IEEE TRANSACTIONS ON IMAGE PROCESSING,33,1285-1298.
MLA Zhou, Pengfei,et al."Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot Food Detection".IEEE TRANSACTIONS ON IMAGE PROCESSING 33(2024):1285-1298.

入库方式： OAI收割

来源：计算技术研究所

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